翻译(生物学)
突变
Ensembl公司
计算机科学
计算生物学
遗传学
人工智能
生物
基因
基因组学
信使核糖核酸
基因组
作者
Javier Castell-Diaz,Francisco Abad-Navarro,María Eugenia de la Morena‐Barrio,Javier Corral,Jesualdo Tomás Fernández-Breis
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-11-01
卷期号:26 (11): 5750-5756
被引量:1
标识
DOI:10.1109/jbhi.2022.3200966
摘要
The effect of mutations has been traditionally predicted by studying what may happen due to the substitution of one amino acid for another one. This approach may be effective for mutations with impact in the function of the protein, but ineffective for mutations in the translation initiation codon. Such mutation might avoid the generation of the protein. Consequently, specific methods for predicting the effect of mutations in the translation initiation codon are needed. We propose a method for predicting the effect of mutations in the canonical translation initiation codon based on a biological model that considers specific features of such mutations, like the distance to a potential alternative initiation codon. Our predictor has been developed using tree-based machine learning algorithms and data extracted from Ensembl . Our final model is able to detect whether a mutation in the canonical initiation codon is deleterious or benign with a precision of 44.28% and an accuracy of 98.32%, which improves the results of state of the art tools such as PolyPhen , SIFT , or CADD for this type of mutation.
科研通智能强力驱动
Strongly Powered by AbleSci AI